Populating an expert-system knowledgebase based on correspondences between knowledgebase axioms and business processes

ABSTRACT

A knowledgebase of an expert system is populated with rules inferred from a set of business processes that govern the manner in which the business interacts with users. Each business process contains an input, an output, an action, and a set of dependency relationships that relate pairs of the input, the output, and the action. Each process&#39;s input, output, action, and dependency relationships are translated, respectively, into a subject, an object, a predicate, and a set of dependency relationships among the subject, object, and predicate, of a natural-language rule. Each rule is stored in the expert system&#39;s knowledgebase as a directed graph, and nodes representing each stored subject, object, and predicate are assigned domain classifications as a function of characteristics of the business rule. These domain classifications are represented within the knowledgebase as a set of domain classifications determined as a further function of characteristics of the business rule.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application claiming priority to Ser.No. 15/0505,586 filed Feb. 23, 2016, now U.S. Pat. No. 10,460,042 issuedOct. 29, 2019, which is a continuation application claiming priority toSer. No. 13/928,495 filed Jun. 27, 2013 now U.S. Pat. No. 9,324,025issued Apr. 26, 2016, the contents of which are hereby incorporated byreference.

TECHNICAL FIELD

The present invention relates to automatically translating a descriptionof a business process into rules for an expert system's natural-languageinteractions with users.

BACKGROUND

An artificially intelligent expert-system computer program may inferrules of behavior from data and logic stored in a knowledgebase thatrepresents characteristics of real-world entities and activities. Anexpert system may use information and logic stored in a knowledge baseto infer appropriate ways to interact with users, using human-likenatural-language communications. But it is difficult to ensure that aknowledgebase accurately captures subtle nuances of, and relationshipsamong, real-world entities and activities because methods ofrepresenting aspects of the real world may not be designed to betranslated seamlessly into a format in which a knowledgebase may bedesigned.

BRIEF SUMMARY

A first embodiment of the present invention provides a method forautomating natural-language interactions between an expert system and auser, wherein the expert system comprises a set of rules for interactingwith the user, the method comprising:

a processor of a computer system automatically translating a firstbusiness process of a set of business processes of a business into afirst rule of the set of rules,

wherein the natural-language communication relates to the first businessprocess; and

wherein the first rule is selected from the group comprising:

a rule for assigning meaning to a natural-language communicationcommunicated from a user to the expert system,

a rule for formulating a natural-language reply to the natural-languagecommunication,

a rule for formulating a natural-language question to the user inresponse to receiving the natural-language communication;

a rule for inferring additional rules from the natural-languagecommunication, and

a rule for performing an other action or subtask in response to thenatural-language communication.

A second embodiment of the present invention provides a computer programproduct, comprising a computer-readable hardware storage device having acomputer-readable program code stored therein, said program codeconfigured to be executed by a processor of a computer system toimplement a method for automating natural-language interactions betweenan expert system and a user, wherein the expert system comprises a setof rules for interacting with the user, the method comprising:

the processor automatically translating a first business process of aset of business processes of a business into a first rule of the set ofrules,

wherein the natural-language communication relates to the first businessprocess; and

wherein the first rule is selected from the group comprising:

a rule for assigning meaning to a natural-language communicationcommunicated from a user to the expert system,

a rule for formulating a natural-language reply to the natural-languagecommunication,

a rule for formulating a natural-language question to the user inresponse to receiving the natural-language communication;

a rule for inferring additional rules from the natural-languagecommunication, and

a rule for performing an other action or subtask in response to thenatural-language communication.

A third embodiment of the present invention provides a computer systemcomprising a processor, a memory coupled to said processor, and acomputer-readable hardware storage device coupled to said processor,said storage device containing program code configured to be run by saidprocessor via the memory to implement a method for automatingnatural-language interactions between an expert system and a user,wherein the expert system comprises a set of rules for interacting withthe user, the method comprising:

the processor automatically translating a first business process of aset of business processes of a business into a first rule of the set ofrules,

wherein the natural-language communication relates to the first businessprocess; and

wherein the first rule is selected from the group comprising:

a rule for assigning meaning to a natural-language communicationcommunicated from a user to the expert system,

a rule for formulating a natural-language reply to the natural-languagecommunication,

a rule for formulating a natural-language question to the user inresponse to receiving the natural-language communication;

a rule for inferring additional rules from the natural-languagecommunication, and

a rule for performing an other action or subtask in response to thenatural-language communication.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the structure of a computer system and computer programcode that may be used to implement a method for automatingnatural-language interactions between an expert system and a user inaccordance with embodiments of the present invention.

FIG. 2 is a flow chart that summarizes an embodiment of a method forautomating natural-language interactions between an expert system and auser in accordance with embodiments of the present invention.

DETAILED DESCRIPTION

The present invention automatically translates a description of abusiness process into information from which an expert system may infera set of rules about how to use natural-language communications tointeract with users about the business process.

An expert system is an artificially intelligent computer program thatemulates cognitive behavior of human beings. Expert systems may interactwith users through natural-language communications, which are freeformverbal interactions that mimic human conversations. The artificialintelligence that underlies an expert system may be represented all orin part by data and logic stored in a data structure known as aknowledgebase.

The contents of a knowledgebase may represent a set of rules (sometimescalled “axioms”) that help an expert system determine appropriate formatand content of its natural-language interactions. A knowledgebase mayalso comprise representations of real-world entities and processes fromwhich such rules may be inferred.

In some embodiments, a knowledgebase may represent rules all or in partas a set of context-sensitive data elements and a set ofcontext-sensitive relationships between pairs of those elements. Aknowledgebase may organize these elements and relationships in manyways, including, but not limited to, combinations of hierarchies, trees,tables, linked lists, semantic chains, networks, databases, and directedor nondirected graphs. In an embodiment wherein a knowledgebase isinternally organized as a directed-graph data structure, thecontext-sensitive elements and relationships may be representedrespectively as nodes and dependencies of the directed graph.

A knowledgebase may represent characteristics of rules by organizingstored nodes into dependencies that imply functions or relationshipsamong the real-world entities and processes represented by those nodes.One method of storing such information is to represent a rule as a“triple” data structure (that is, a data item that comprises threeordered elements). A knowledgebase may store a set of such triples instructure known as a “triple store.”

The exact content and internal organization of a knowledgebase and of aknowledgebase component like a triple store are implementation-dependentand may be a function of a purpose of the expert system, of a context(or “domain”) of the subject matter of the expert system, of a platformupon which the expert system is implemented, or of a combination ofthese or other design-dependent, business-dependent, andimplementation-dependent factors.

Creating an expert system that may converse with users about one or morebusiness activities may require designing and populating all or part ofa knowledgebase components with representations of the one or morebusiness processes. Because management-science experts, who analyze andmodel business processes, generally do not work in the field ofartificial intelligence, the task of designing and implementing anexpert system that understands the inner workings of a business mayinvolve ad hoc, informal, or inconsistent methods of translating aninitial representation of the one or more business processes into arepresentative form that may be stored in a knowledgebase. In somedevelopment efforts, at least some transitional or interim translationscan only be performed manually or without benefit of a common interfacestandard.

Designing such an expert system may thus be a cumbersome,nonstandardized procedure that is vulnerable to errors and omissions.Furthermore, using such ad hoc methods to populate a knowledgebase withinformation about a set of related business activities may make it hardto accurately identify and represent subtle relationships among elementsof those activities.

Embodiments of the present invention comprise a method of automaticallytranslating a standardized representation of a business process into aform that may be stored in a knowledgebase. Such stored information mayrepresent all or part of the expert system's understanding of thebusiness process and may comprise, or facilitate an inference of, rulesthat help the expert system accurately determine how to communicate withusers during interactions related to the business process. The use ofstandardized, analogous modeling formats makes possible an automatedtranslation procedure that may automatically generate expert-systemrules (or corresponding knowledgebase data) as a function of arepresentation of a business process.

Within the scope of this document, a business process may describe oneor more business activities. A business activity may comprise, but isnot limited to, an activity that a business directs toward producing orsupporting a service or product for one or more customers, or anactivity related to an internal operation or function of a business,such as human-resource management, financial analysis, manufacturing,quality control, legal compliance, accounting, or executive management.A business process may further comprise one or more core businesscharacteristics, operations, or functions, such as a business'sorganizational structure or its methods of creating revenue.

A business process may be broken down into a set of one or more relatedactivities that a business directs toward producing a service or productfor a particular customer or customers. Such processes may comprise, butare not limited to: an operational process that implements a primaryoperation of an enterprise, such as manufacturing, logistics, or sales;a management process, such as an executive-management policy or aline-supervision procedure; a support process, which may furthercomprise peripheral functions related to specific departmental unitslike Human Resources or Customer Support; or an other core process ofthe business.

A business process may be represented in many ways, including, but notlimited to, representation in a business-process model, where thebusiness-process model is an information structure that may formally orinformally represent characteristics of one or more business processes.Business-process models may take many forms and may be structured,formatted, or organized in many ways, sometimes comprising bothstructured and unstructured stores of data.

A business-process model might, for example, comprise formal or informalrepresentations of a business's policies, procedures, service offeringsor product lines, organizational structures, security framework, orworkflows. Such a model may also comprise information from which adomain or context of these representations may be inferred, as well asrepresentations of dependencies and other relationships among elementsof the represented business activities.

The business-process model may thus comprise any type of logic or datathat might help an expert system infer a rule that determines, supports,guides, or otherwise relates to natural-language user interactions aboutan activity represented by the model. Such inferring may furthercomprise inferring an addition, revision, deletion, or replacement of arule of the expert system, and may be performed with or withoutconsideration of extrinsic information that may include, but is notlimited to, a natural-language interaction with a user, external sourcesof documentation, other informational sources, or a logical conclusiondrawn as a function of combinations of any of these resources.

Embodiments of the present invention may associate a business-processmodel representation of a business process, or of a component activityof a business process, as comprising three types of elements: i) one ormore input entities; ii) one or more output entities; and iii) afunction or action. Associating such a three-element representation withone or more structurally similar three-element representations stored inan expert-system's knowledgebase may facilitate an automated translationof the business process or activity representation into a form fromwhich an expert-system may infer a rule for user interaction related tothe process or activity.

A business-process model that represents a business process or acomponent activity of a business process, may thus comprise three typesof elements of the business process. These three types of elements mayrespectively identify one or more instances of: i) an acting or inputentity; ii) an action or function performed by the acting entity orperformed upon the input entity; and iii) an output entity thatidentifies a result of performing the action upon the input entity orthat identifies an entity upon which an acting entity performs theaction.

In some embodiments, a representation may comprise only one instance ofeach of the three types of elements. In some embodiments, elements maybe organized by type of element an ordered 3-tuple, or “triple” and eachmember of the three members of the triple may in turn comprise more thanone element. In such embodiments, the three members of each triple maybe stored in any order. But the members and the elements that compriseeach member should be stored according to a consistent, predictablepattern in order to facilitate a reliable translation of the contents ofthe business-process model into a format that is compatible with that ofa knowledgebase.

In an example wherein a computer-diagnostic business process comprisesone instance of each type of element, an activity of the process mightbe represented in a business-process model as a triplet (technician,diagnose, diagnosis). In other embodiments, wherein context, a designgoal, a business priority, or an other factor might suggest an otherrepresentation, the process might be represented as (technician,diagnose, computer) or (Error Code A03, interpret error code, hard-drivefailure). In yet another example, a production of a quarterlymanufacturing report might be represented as a triplet (daily productionfigures, data aggregation, quarterly report).

Depending on how a business conceptualizes and organizes its operationsand components, a business-process model may represent more than onebusiness process, a business process may comprise more than one businessactivity, and a business activity may comprise more than one businessprocess.

Business processes and the elements that comprise them may be organizedor related by dependencies or by other types of relationships, and thosedependencies and other types of relationships may be represented withina business-process model or across more than one business-process modelby organizing represented processes, activities, and elements into datastructures. These data structures may comprise, but not limited to,combinations of hierarchies, trees, tables, linked lists, semanticchains, networks, databases, and directed or nondirected graphs.

A behavioral rule of an expert system, or information stored in theexpert system's knowledgebase from which the rule may be inferred, maybe similarly conceptualized represented, and organized into datastructures. These data structures may organize information in aknowledgebase as a function of a dependency or other relationshipbetween analogous business processes, activities, or elementsrepresented in one or more business-process models.

A triple store of a knowledgebase may comprise one or more triplesorganized into a data structure, wherein the triples comprise elementsthat correspond to, or are otherwise a function of, one or more of thebusiness process's acting or input entities, functions or actions, oroutput entities. Furthermore, members of the triples stored in theknowledgebase may be organized within a data structure in ways that maybe functions of inter-element relationships comprised by abusiness-process model. A knowledgebase that comprises a triple storedata repository, where the repository is formatted and structuredaccording to embodiments of the present invention, may thus represent abusiness process that is represented in one or more business-processmodels formatted and structured according to embodiments of the presentinvention.

In an example of how a knowledgebase may be formatted and structuredaccording to embodiments of the present invention, consider aknowledgebase, defined within a context (the knowledgebase's “domain”)of a company's computer network, that represents concepts:

user

computer

memory

installed memory

memory usage.

These concepts might be related by a set of three-element (input,function, output) rules, which may be encoded into the knowledgebase astriple data items and sets of relationships or dependencies amongelements of the triple data items. Such rules might be logically statedas:

user may use computer

memory may be installed

computer may contain installed memory

user may monitor usage of installed memory.

In practice, the encoding into the knowledgebase may store these logicalstatements in a form and organize them into a data structure known tothose skilled in the art of expert-system design. The form and datastructure should, nonetheless, preserve or facilitate anelement-by-element correspondence with a format used to representanalogous logic in the business-process model.

In such embodiments, a rule, logical premise, or statement offunctionality stored or encoded into a knowledgebase may be representedby a type of triple data structure that comprises a predicate, asubject, and an object, wherein the predicate identifies an action orfunction; the subject performs the predicate action or function or is aninput to the predicate action or function; and the object is a result ofperforming the predicate action or function or is an entity upon whichthe predicate action or function is performed.

Such a conceptualization and format are analogous to a three-elementrepresentation, as described above, of a business process or activity ina business-process model that conforms to embodiments of the presentinvention. The statement “user may monitor usage of installed memory,”for example, might be analogous to a business-process modelrepresentation “(user, monitor usage, installed memory)”.

Here, a logical premise extracted from a business process or activity isrepresented and stored in a knowledgebase in a way that lets an expertsystem infer one or more rules of interaction, where these one or morerules may help an expert system identify a logical premise, draw aconclusion, interpret or compose natural language statements, or make another decision related to interactions about the business process oractivity.

In some embodiments, the rule, logical premise, or statement offunctionality stored in a knowledgebase may be represented or expressedin other syntactical forms. An axiom that expresses a logicalrelationship “a user monitors memory usage,” for example, may beexpressed as “monitors (user, memory usage),” where the triple'spredicate would be “monitors,” its subject “user,” and its object“memory usage.” The same logical relationship might also be expressed inalternate functional notation as the expression “memoryusage hasmonitoruser.” Other variations are possible, so long as the format ofinformation stored in the knowledgebase is relatable, through anautomated translation or other function, to a format used to representanalogous information stored in a business-process model.

It is known in the art that the behavior of an artificially intelligentexpert system when interacting with a user may be all or partlydetermined as a function of inferences drawn from data and logic storedin a knowledgebase. By automatically translating information and logicstored in a set of business-process models into a structuredknowledgebase, embodiments of the present invention thus provide anautomated method by which an expert system's natural-language userinteraction related to a business process may be all or partlydetermined as a function of inferences drawn from an appropriate modelof the business process.

Embodiments of the present invention may similarly preserve andtranslate relationships among business processes and relationships amongelements of business processes. This preservation and translation may beaccomplished by organizing triple data structures stored in a triplestore of a knowledgebase into data structures that are analogous to theways in which elements of business processes are organized in one ormore corresponding business-process models. Such knowledgebase andbusiness-process model organizations and data structures may comprise,but are not limited to, combinations of hierarchies, trees, tables,linked lists, semantic chains, networks, databases, and directed ornondirected graphs.

In one example, wherein two triples are organized into a knowledgebasesdata structure that links, denotes a dependency, implies a condition,or otherwise relates the two triples, such a data structure may take aform:

monitor (user, memory usage)→imply (memory usage, installed memory)

in order to express a conditional rule: “If a user monitors memoryusage, then memory usage must exist; and the existence of memory usageimplies that memory must be installed” or, in a simpler forms, “A usercannot monitor memory usage unless memory is installed.” An expertsystem that conforms to embodiments of the present invention might useor infer logic implied by these related triples, possibly augmented byrelated rules, by other known facts, by other user interactions, bycontext, or by other extrinsic information sources, to construct anatural-language answer to a user's natural-language question “Is memoryinstalled in my computer?”

Such an organization of knowledgebase triples may be generatedautomatically, by means of an embodiment of the present invention, as afunction of one or more representations, dependencies, or datastructures comprised by one or more business process models, where theone or more models describe an analogous relationship or dependencybetween business processes, activities, or elements analogous toentities represented by members of the two knowledgebase triples.

Embodiments of the present invention allow expert-system designers topreserve nuances of relationships among elements of business processesand activities and to preserve those nuances during a translation of abusiness-process model into a component of an expert-systemknowledgebase. Because business-process models and expert systems aregenerally developed by different persons who are skilled in differentfields of art, a subtle characteristic of a business process or activitythat may be an indirect function of relationships among elements of oneor more business processes or activities may be omitted from a design ofan expert system that would otherwise address aspects of the one or morebusiness processes or activities.

Relationships among elements of a business's business processes andactivities may be manually recorded in a business-process model by themodel's designer or implementer. Embodiments of the present inventionmay use known techniques and technologies, which may include, but arenot limited to, methods of data-mining, semantic analytics, andsyntactical analytics, to automatically identify further relationshipsamong elements of business processes and activities, to optionally embedthose relationships into a business-process model that conforms toembodiments of the present invention, and to ensure that those subtlerelationships are accurately translated into a form from which an expertsystem may infer appropriate rules of interacting with users innatural-language communications related to a characteristic of thebusiness processes and activities.

In one example, consider a business that employs the followingprocedural activity to process a customer's application to open anaccount:

1. Collect applicant information

2. Obtain written consent from applicant to verify credit score(dependent upon 1)

3. Verify applicant's credit score (dependent upon 2)

4. Does credit score exceed threshold? (dependent upon 3)

5. Create account (dependent upon 4)

6. Notify applicant of account creation (dependent upon 5)

7. Notify applicant of account denial (dependent upon 4)

The actions and dependency relationships comprised by these steps may begenerated automatically, or may manually culled from sources likeemployee interviews, internal business documentation, processdescriptions within business process models, product manuals, flowchartsand other drawings, or industry models and standards. In some cases, aparticular action or dependency relationship may be identified ordeduced from combinations of automatic and manual sources, includinginferred relationships among elements of other steps in the sameprocedure. For example, it may be deduced from the dependency of step 6upon step 5 and the dependency of step 5 upon step 4, that theperformance of step 6 must depend upon the outcome of step 4.

This representation of the application procedure may be translated intoa representation stored in one or more business-process models. In thisexample, such a representation might be in the form of a functionalnotation: “<action>(<input><output>).”

Steps 1 and 2, for example, might be identified by means of interviewingemployees of the business's Supervisor pool, resulting in abusiness-process model representation:

collect (supervisor, applicantInfo)

getConsent (supervisor, applicant)

A functional representation of step 3 may be identified by means ofinterviews with the business's Credit or Sales Department, but anintrinsic, deducible, dependency between steps 3 and 4 may result insteps 3 and 4 being represented by a single functional statement:

verifyCredit (applicant,approval)

Other steps of the activity and functional representations of thosesteps may be identified in similar ways, relying on sources that mayinclude, but are not limited to, employee interviews, existingknowledgebases, written procedures, deducible conclusions, or otherextrinsic documentation.

In a similar manner, other dependencies, preconditions, or otherrelationships among steps may be documented or inferred and representedin a business-process model. Step 2, for example, might further comprisean implicit function:

notarysign (applicant, consentForm)

and this notarysign function may be a precondition for step 3 if theapplicant's credit score may be verified only after the unusual step ofa notary public notarizing the applicant's written consent to a creditcheck. Such an important, but easily omitted, step and its intrinsicdependency relationships may be lost when an expert system forprocessing credit applications is designed manually. But embodiments ofthe present invention, by ensuring that such relationships are preservedin a business-process model as components of a translatable datastructure, further ensure that every such dependency is translated intoa logical dependency in an expert-system's knowledgebase.

In some embodiments, as described below, such relationships may bepreserved by organizing a business-process model and a triple store of aknowledgebase into a pair of analogous first and second directed-graphdata structures. In such an embodiment, elements of a business processare represented as nodes of a first directed graph in thebusiness-process model and relationships among those elements arerepresented as dependencies between pairs of nodes of the first directedgraph; classes of concepts and activities represented by thebusiness-process model are further represented as nodes of the firstdirected graph, and relationships among those classes are furtherrepresented as dependencies between pairs of nodes of the first directedgraph; and analogous members of knowledgebase triples and analogousrelationships among those analogous members (where the analogous membersand the analogous relationships are respectively analogous to nodes anddependencies of the first directed graph) are represented in a triplestore of the knowledgebase as nodes and dependencies of the seconddirected graph.

Embodiments of the present invention may thus automatically generatebehavioral rules, or stored information from which such rules may beinferred, appropriate to a context of a particular business process byautomatically translating all or part of a model of that businessprocess into an analogous knowledgebase, where an expert system may usecontents of the knowledgebase to infer ways to properly interpret andrespond to natural-language user interactions.

The structures and formats illustrated here are not intended to implyconstraints upon the types of representations and formats that arewithin the scope of the present invention. The method of the presentinvention may be used with expert systems and business-process modelsthat comprise other types, classes, implementations, or representationsof business processes, axioms, triples, triple stores, rules, inferenceengines, and other components of a business-process model and of anexpert system.

FIG. 1 shows a structure of a computer system and computer program codethat may be used to implement a method for automating natural-languageinteractions between an expert system and a user in accordance withembodiments of the present invention. FIG. 1 refers to objects 101-115.

Aspects of the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module,” or “system.” Furthermore,in one embodiment, the present invention may take the form of a computerprogram product comprising one or more physically tangible (e.g.,hardware) computer-readable medium(s) or devices havingcomputer-readable program code stored therein, said program codeconfigured to be executed by a processor of a computer system toimplement the methods of the present invention. In one embodiment, thephysically tangible computer readable medium(s) and/or device(s) (e.g.,hardware media and/or devices) that store said program code, saidprogram code implementing methods of the present invention, do notcomprise a signal generally, or a transitory signal in particular.

Any combination of one or more computer-readable medium(s) or devicesmay be used. The computer-readable medium may be a computer-readablesignal medium or a computer-readable storage medium. Thecomputer-readable storage medium may be, for example, but is not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium or device may include the following: anelectrical connection, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or flash memory), Radio FrequencyIdentification tag, a portable compact disc read-only memory (CD-ROM),an optical storage device, a magnetic storage device, or any suitablecombination of the foregoing. In the context of this document, acomputer-readable storage medium may be any physically tangible mediumor hardware device that can contain or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signalwith computer-readable program code embodied therein, for example, abroadcast radio signal or digital data traveling through an Ethernetcable. Such a propagated signal may take any of a variety of forms,including, but not limited to, electro-magnetic signals, optical pulses,modulation of a carrier signal, or any combination thereof.

Program code embodied on a computer-readable medium may be transmittedusing any appropriate medium, including but not limited to wirelesscommunications media, optical fiber cable, electrically conductivecable, radio-frequency or infrared electromagnetic transmission, etc.,or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including, but not limited to programminglanguages like Java, Smalltalk, and C++, and one or more scriptinglanguages, including, but not limited to, scripting languages likeJavaScript, Perl, and PHP. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer, or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN), awide area network (WAN), an intranet, an extranet, or an enterprisenetwork that may comprise combinations of LANs, WANs, intranets, andextranets, or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

Aspects of the present invention are described above and below withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according toembodiments of the present invention. It will be understood that eachblock of the flowchart illustrations, block diagrams, and combinationsof blocks in the flowchart illustrations and/or block diagrams of FIGS.1-4 can be implemented by computer program instructions. These computerprogram instructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmabledata-processing apparatus to produce a machine, such that theinstructions, which execute via the processor of the computer or otherprogrammable data-processing apparatus, create means for implementingthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

These computer program instructions may also be stored in acomputer-readable medium that can direct a computer, other programmabledata-processing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture, including instructions thatimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data-processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce acomputer-implemented process such that the instructions that execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart illustrations and/or block diagrams FIGS. 1-4 illustratethe architecture, functionality, and operation of possibleimplementations of systems, methods and computer program productsaccording to various embodiments of the present invention. In thisregard, each block in the flowchart or block diagrams may represent amodule, segment, or portion of code, wherein the module, segment, orportion of code comprises one or more executable instructions forimplementing one or more specified logical function(s). It should alsobe noted that, in some alternative implementations, the functions notedin the block may occur out of the order noted in the figures. Forexample, two blocks shown in succession may, in fact, be executedsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustrations, and combinations of blocks in the block diagrams and/orflowchart illustrations, can be implemented by special-purposehardware-based systems that perform the specified functions or acts, orcombinations of special-purpose hardware and computer instructions.

In FIG. 1, computer system 101 comprises a processor 103 coupled throughone or more I/O Interfaces 109 to one or more hardware data storagedevices 111 and one or more I/O devices 113 and 115.

Hardware data storage devices 111 may include, but are not limited to,magnetic tape drives, fixed or removable hard disks, optical discs,storage-equipped mobile devices, and solid-state random-access orread-only storage devices. I/O devices may comprise, but are not limitedto: input devices 113, such as keyboards, scanners, handheldtelecommunications devices, touch-sensitive displays, tablets, biometricreaders, joysticks, trackballs, or computer mice; and output devices115, which may comprise, but are not limited to printers, plotters,tablets, mobile telephones, displays, or sound-producing devices. Datastorage devices 111, input devices 113, and output devices 115 may belocated either locally or at remote sites from which they are connectedto I/O Interface 109 through a network interface.

Processor 103 may also be connected to one or more memory devices 105,which may include, but are not limited to, Dynamic RAM (DRAM), StaticRAM (SRAM), Programmable Read-Only Memory (PROM), Field-ProgrammableGate Arrays (FPGA), Secure Digital memory cards, SIM cards, or othertypes of memory devices.

At least one memory device 105 contains stored computer program code107, which is a computer program that comprises computer-executableinstructions. The stored computer program code includes a program thatimplements a method for automating natural-language interactions betweenan expert system and a user in accordance with embodiments of thepresent invention, and may implement other embodiments described in thisspecification, including the methods illustrated in FIGS. 1-4. The datastorage devices 111 may store the computer program code 107. Computerprogram code 107 stored in the storage devices 111 is configured to beexecuted by processor 103 via the memory devices 105. Processor 103executes the stored computer program code 107.

Thus the present invention discloses a process for supporting computerinfrastructure, integrating, hosting, maintaining, and deployingcomputer-readable code into the computer system 101, wherein the code incombination with the computer system 101 is capable of performing amethod for automating natural-language interactions between an expertsystem and a user.

Any of the components of the present invention could be created,integrated, hosted, maintained, deployed, managed, serviced, supported,etc. by a service provider who offers to facilitate a method forautomating natural-language interactions between an expert system and auser. Thus the present invention discloses a process for deploying orintegrating computing infrastructure, comprising integratingcomputer-readable code into the computer system 101, wherein the code incombination with the computer system 101 is capable of performing amethod for automating natural-language interactions between an expertsystem and a user.

While it is understood that program code 107 for automatingnatural-language interactions between an expert system and a user may bedeployed by manually loading the program code 107 directly into client,server, and proxy computers (not shown) by loading the program code 107into a computer-readable storage medium (e.g., computer data storagedevice 111), program code 107 may also be automatically orsemi-automatically deployed into computer system 101 by sending programcode 107 to a central server (e.g., computer system 101) or to a groupof central servers. Program code 107 may then be downloaded into clientcomputers (not shown) that will execute program code 107.

Alternatively, program code 107 may be sent directly to the clientcomputer via e-mail. Program code 107 may then either be detached to adirectory on the client computer or loaded into a directory on theclient computer by an e-mail option that selects a program that detachesprogram code 107 into the directory.

Another alternative is to send program code 107 directly to a directoryon the client computer hard drive. If proxy servers are configured, theprocess selects the proxy server code, determines on which computers toplace the proxy servers' code, transmits the proxy server code, and theninstalls the proxy server code on the proxy computer. Program code 107is then transmitted to the proxy server and stored on the proxy server.

In one embodiment, program code 107 for automating natural-languageinteractions between an expert system and a user is integrated into aclient, server and network environment by providing for program code 107to coexist with software applications (not shown), operating systems(not shown) and network operating systems software (not shown) and theninstalling program code 107 on the clients and servers in theenvironment where program code 107 will function.

The first step of the aforementioned integration of code included inprogram code 107 is to identify any software on the clients and servers,including the network operating system (not shown), where program code107 will be deployed that are required by program code 107 or that workin conjunction with program code 107. This identified software includesthe network operating system, where the network operating systemcomprises software that enhances a basic operating system by addingnetworking features. Next, the software applications and version numbersare identified and compared to a list of software applications andcorrect version numbers that have been tested to work with program code107. A software application that is missing or that does not match acorrect version number is upgraded to the correct version.

A program instruction that passes parameters from program code 107 to asoftware application is checked to ensure that the instruction'sparameter list matches a parameter list required by the program code107. Conversely, a parameter passed by the software application toprogram code 107 is checked to ensure that the parameter matches aparameter required by program code 107. The client and server operatingsystems, including the network operating systems, are identified andcompared to a list of operating systems, version numbers, and networksoftware programs that have been tested to work with program code 107.An operating system, version number, or network software program thatdoes not match an entry of the list of tested operating systems andversion numbers is upgraded to the listed level on the client computersand upgraded to the listed level on the server computers.

After ensuring that the software, where program code 107 is to bedeployed, is at a correct version level that has been tested to workwith program code 107, the integration is completed by installingprogram code 107 on the clients and servers.

One or more data storage units 111 (or one or more additional memorydevices not shown in FIG. 1) may be used as a computer-readable hardwarestorage device having a computer-readable program embodied thereinand/or having other data stored therein, wherein the computer-readableprogram comprises stored computer program code 107. Generally, acomputer program product (or, alternatively, an article of manufacture)of computer system 101 may comprise said computer-readable hardwarestorage device.

FIG. 2 is a flow chart that summarizes an embodiment of a method forautomating natural-language interactions between an expert system and auser in accordance with embodiments of the present invention. FIG. 2comprises steps 201-211.

The method of FIG. 2 automatically and dynamically translates a set ofrepresentations of one or more business processes of one or morebusinesses into a set of rules, or into representations from which canbe inferred a set of rules, where one or more of the rules allow anexpert system to infer appropriate ways to interpret and respond to anatural-language communication from a user, and where the communication,interpretation, or response relates to a characteristic of one or moreof the business processes. Such rules or representations may beorganized, along with other information used to determine behaviors ofthe expert system, into a data structure stored as all or part of aknowledgebase, where the structure, contents, or internal organizationof the knowledgebase may be a function of the structure, contents, orinternal organization of the representations of the one or more businessprocesses.

In the embodiment of FIG. 2, the representations of the one or morebusiness processes may comprise one or more business-process models.

Step 201 identifies one or more business processes about which anartificially intelligent expert system will be designed to interact withusers. These interactions may comprise actions that may comprise, butare not limited to, interpreting a user's natural-language communicationabout a characteristic of the one or more business processes, replyingto the communication with a natural-language reply, question, or othercommunication, using the communication to infer additional knowledge toadd to the knowledgebase, performing an action or subtask specified byrepresentations of the one or more business processes comprised by theknowledge.

This identification of the one or more business processes may be afunction of a combination of manual and automated analytical steps thatmay comprise, but are not limited to, identifying, classifying, orcharacterizing an activity, function, standard, operation, procedure, orother characteristic of one or more business processes, activities, orelements of one or more business processes or activities of one or morebusinesses.

In some embodiments, the one or more business processes may be selectedby means known to those skilled in an art related to management science,business management, process automation, information science, andsimilar fields, or may be selected according to criteria known to thoseskilled in these arts. For example, a procedure-automation specialistmight analyze a business's work flows and identify or select processesto be automated on the basis of those processes' relationships to acritical path of the business's manufacturing workflows.

In some embodiments, a business process may be represented as comprisingone or more distinct business activities or functions. In someembodiments a business activity or function may be represented ascomprising one or more business processes.

Step 203 represents the one or more business processes as one or morebusiness-process models. A business-process model may take many forms,but in some embodiments of FIG. 2, each activity or function associatedwith one of the business processes is represented in a form that, asdescribed above, comprises instances of three types of elements: i) anacting or input entity; ii) an action or function that is performed bythe acting entity or that is performed upon the input entity; and iii)an output entity that identifies a result of performing the action orfunction upon the input entity or that identifies an entity upon whichthe acting entity performs the action or function.

A business-process model may further comprise a set of conceptclassifications. In some embodiments, an acting entity, input entity,output entity, or other element of a business process or activity may beassociated with one or more distinct classes of concepts, where each ofthese elements may be associated with different classes.

In some embodiments, a business-process model may further comprise a setof activity classes, wherein each activity class is associated with anaction or function element of a business process or of abusiness-process activity, and wherein the business-process model maycomprise dependencies or other relationships among one or more activityclasses and one or more concept classifications.

In some embodiments, a business activity, process, or function maycomprise multiple instances of each of these three types of elements. Insome embodiments, a business activity, process or function may berelated to other activities, processes, or function through a dependencyrelationship or other type of relationship. Some or all of thesedependency relationships or other types of relationships may berepresented, as described below, in the one or more business-processmodels.

In one example, a business-process model representation (technician,diagnose hardware, computer), comprising a technician acting entity, adiagnose hardware action, and a computer output entity, may associatethe technician entity with an Employee concept classification, furtherassociate the diagnose hardware action with a Maintenance Task conceptclassification, and associate the computer entity with an ElectronicsAsset concept classification.

In some embodiments, this entire triple may be related to thebusiness-process model triple representation (hardware failure, onlinereport, repair ticket), which identifies an online-reporting businessprocess that generates a repair ticket when a hardware failure isdetected. This relationship may take many forms, which may comprise, butare not limited to, a dependency between the two triples wherein thehardware-diagnosis business process represented by the first tripledepends upon (that is, cannot be performed until) an occurrence of theonline-reporting business process.

In some embodiments, a relationship may exist between elements of thesame or different business-processes. It is possible, for example, thatan instance of the “diagnose hardware” element of the first triple mayexist only if there exists a corresponding instance of the “repairticket” element.

Dependencies and other types of relationships may also exist betweenconcept classifications. A concept classification “Repair Task,” forexample, may depend upon a concept classification “Equipment Failure,”meaning that an element of a business-process model triple thatrepresents an instance of class Repair Task cannot be created unlessthere is a corresponding element that represents an instance of classEquipment Failure. Such a rule would represent a logical premise that abusiness would not allow a repair task to be initiated unless anequipment failure has occurred and would spawn a dependency relationshipbetween a pair of elements of one or more triples, where a first elementof the triple is associated with concept classification Repair Task anda second element of the triple is associated with concept classificationEquipment Failure.

Dependencies and other types of relationships may also exist between anactivity class and other elements of a business-process model. In someembodiments, an action or function element of a triple may be associatedwith an activity class and in the example above, the function element ofthe triple (technician, diagnose hardware, computer) might be associatedwith an activity class “Troubleshoot.” In various implementations, thisTroubleshoot classification might be related via a dependency or othertype of relationship, to other activity classes, such as “IdentifyFailure,” “Report Failure,” or “Resolve Failure,” or might be related insome way too a concept classification, such as “Capital Equipment” or“Electronic Assets.”

In all these cases, a relationship among a combination of conceptclassifications and activity classes may spawn an analogous relationshipamong other elements of a business-process model, when the otherelements are instances of entities associated with the related classes.

The structure and organization of these relationships and theidentification and organization of concept and activity classes areimplementation-dependent details of the one or more business modelsgenerated in this step. Methods of representing such relationships andentity classes may be known to those skilled in the arts of analytics,data storage, or related fields. In embodiments of the presentinvention, a goal of a choice of method of this representing is toaccurately describe the organization of and relationships among elementsof business processes and activities selected in step 201 in such a waythat those elements and relationships may be accurately andautomatically translated into a form that may be stored in aknowledgebase.

At the completion of step 203, one or more business-process models mayhave been created that represent all or some of the business processesof one or more businesses, and where a represented business process maybe represented by at least one triple comprised of an acting or inputentity; an action or function; and an output entity. The one or morebusiness-process models may also comprise representations ofrelationships among elements of the business processes andrepresentations of relationships among classificationifications ofelements of the represented business processes.

In some embodiments, some of the representations comprised by the one ormore business-process models will not be in the form of a triple datastructure (that is, as an item that comprises an ordered list of threenull or non-null data elements). In such embodiments, some or all of theelements of the business processes will be represented or organized inan other form, from which logic or data may be extracted in order torepresent, in a knowledgebase of an expert system, a business process oran activity of a business process.

In some embodiments, step 203 may comprise performing a parsing functionthat may be known to those skilled in the relevant arts. Such a parsingfunction may generate parsed output that represents elements of one ormore business processes or business-process activities.

In some embodiments, parsing a first business process or a firstbusiness-process activity may comprise automatically identifying a firstinput or acting entity, a first action or function, and a first outputentity, where these three identified elements are associated with thefirst business process or activity. In certain cases, the parsing mayinstead omit identifying one or more of the first input or actingentity, the first action or function, and the first output entity.

In some embodiments, the parsing may further comprise automaticallyidentifying a set of classifications, wherein the first acting or inputentity may be associated with a first classification of the set ofclassifications, the first function or action may be associated with asecond classification of the set of classifications, and the firstoutput entity may be associated with a third classification of the setof classifications. In some embodiments, the first and thirdclassifications may be concept classifications and the secondclassification may be an activity classification.

The parsing may comprise any parsing method or technology known to thoseskilled in the art of computer science. The choice and implementation ofa parsing method may be implementation-dependent or may be a function ofa combination of considerations that may comprise, but are not limitedto, a content of or an internal organization of a business-processmodel, a business-process, a business-process activity, a characteristicof an expert-system or of a knowledgebase of an expert system, a formatin which a rule is represented or stored by the expert system, a goal ofa system designer, or a relationship between a characteristic of abusiness-process model generated in step 203 and the expert system orthe knowledgebase of the expert system.

Step 205 translates each triple generated in step 203, or translatescombinations of information-comprising data structures generated ororganized in step 203, into a one or more data structures, where the oneor more data structures are a function of relationships among elementsof the one or more business processes represented in thebusiness-process model.

The data structure created in this step may comprise representations ofrelationships such as dependencies, hierarchical relationships, or otherrelationships among the one or more business processes; dependencies,hierarchical relationships, or other relationships among elements of asubset of the one or more business processes; dependencies, hierarchicalrelationships, or other relationships among classifications that may beassociated with elements of the one or more business processes; andother types of dependencies, hierarchical relationships, or otherrelationships among elements of information comprised by the informationgenerated in the preceding steps of FIG. 2.

In some embodiments, the data structure may comprise a set ofrelationships, comprising but not limited to, dependencies, tree orgraph relationships, or other hierarchical relationships, among one ormore classifications. These relationships may be created in this step inorder to represent analogous relationships among business processes,component business-process activities, or elements of business processesor component business activities.

Methods of generating such data structures may be known to those skilledin the relevant arts. Other methods may be used, selected as a functionof a design goal, a platform characteristic, a business goal, acharacteristic of a domain or context, or other implementation-relatedfactors, to create the data structure as a way of representing a logicalorganization of characteristics of the one or more business processes ina way that facilitates translation of these representations into aknowledgebase of an expert system.

In some embodiments, these one or more data structures comprise a set ofdirected graphs, wherein elements of business processes andclassifications of elements may be represented as nodes of the directedgraphs and relationships among elements and classifications may berepresented as dependencies between pairs of nodes.

Step 207 automatically translates the organized, structured set ofbusiness-process models generated in steps 203 and 205 into a set ofrules, where one or more rules may be represented as one or more tripledata structures. In some embodiments, the models are translated into anintermediate “precursor” form compatible with the format of aknowledgebase, from which an expert system may infer one or more rules.

There may be a many-to-many relationship between members of the set ofrules and members of the set of business-process models, businessprocesses represented by the business-process models, businessactivities represented by the business-process models, or elements ofthe business processes or business activities represented by thebusiness-process models. That is, one rule (or rule precursor) mayrepresent a characteristic of a combination of more than one element ofa business-process model, business process, or business-processactivity, and one business-process model, business process, orbusiness-process activity may be represented by one or more rule orprecursor.

A triple generated in this step may comprise, as described above,instances of three types of elements, a subject, a predicate, and anobject, which may be analogous to three member elements (an acting orinput entity, an action or function, and an output entity) of a triple,generated in previous steps of FIG. 2, that represent all or part of abusiness process or business-process activity.

Step 209 organizes the triples generated in step 207 into a datastructure that is a function of the data structure described in step205. The data structure generated in step 209 may place the triplesgenerated in step 207 into a final form that is appropriate forincorporation into the knowledgebase of an expert system, if they arenot already in such a final form, or may place them into a form that iscompatible with a component of a knowledgebase, such as a knowledgebaseontology or a structured set of axioms.

As noted above in the description of step 205, the data structure ofstep 205 may be a function of relationships among combinations ofbusiness processes, business-process activities, elements of one or morebusiness processes or activities, and concept and activityclassifications, where these relationships represent aspects of theoperation of the one or more business processes identified in step 201.

The data structure generated in step 209 may comprise combinations ofdata structures that are known to those skilled in the art to becompatible with, or to facilitate, the operation of the expert system,where the contents and internal organization of this data structureallow the expert system to use the data structure and its contents toinfer rules for natural-language interaction with users. Such datastructures may comprise combinations of hierarchies, trees, tables,linked lists, semantic chains, networks, databases, and directed ornondirected graphs, or other types of data structures that identifyrelationships among nodes of the data structure, where a node of thedata structure generated in step 209 may comprise a triple generated instep 207, an element of a triple generated in step 207, or aclassification associated with an element of a triple generated in step207.

At the conclusion of step 209, a knowledgebase of an expert system willcomprise an organized, structured set of triples, wherein the members ofthe set of triples describe all or part of a set of rules of behavior ofthe expert system, or represent data and logic from which may beinferred all or part of the set of rules of behavior, and wherein theorganization or structure of the set of triples representsrelationships, including but not limited to dependency relationships,among the all or parts of the rules of behavior represented by thetriples.

Furthermore, each triple and its organization within the knowledgebasedata structure may represent three types of elements and relationshipsamong the three types of elements, where those elements andrelationships are a function of an automatic translation of a set ofcorresponding elements and relationships represented in the one or morebusiness-process models generated in step 203. Stated more simply, eachknowledgebase triple generated in step 209 represents logic from whichmay be inferred an expert-system rule that is a function of all or partof a business process identified in step 201.

Similarly, because an internal organization or structure of a datastructure generated in step 209 is a function of an internalorganization or structure of a data structure generated in step 205, theorganization of the knowledgebase representations generated in step 209is a function of the relationships, as represented by the data structureof step 205, among elements and classifications of business processesand activities identified in step 201.

In step 211, the expert system uses the contents, organization, andstructure of the knowledgebase generated in step 209 to infer rules forinteracting with users in ways that are related to the one or morebusiness processes. This interacting may comprise, but is not limitedto, interpreting a natural-language statement, natural-language query,or other natural-language communication communicated by a user; replyingto the natural-language communication; formulating a natural-languageresponse to the natural-language communication; or using thenatural-language communication to formulate an other natural-languagecommunication for the purpose of communicating the other communicationto the user, to infer additional knowledge in order to complement orsupplement the contents of the knowledgebase, or to perform an action orsubtask specified by any component of the one or more businessprocesses.

In summation, embodiments of the present invention disclose a method offormatting and structuring representations of elements, and ofrelationships among elements, of a set of business processes. Thismethod of formatting and structuring allows these representations to beautomatically translated into a form that is compatible with aknowledgebase of an expert system, and from which the expert system mayinfer rules of behavior when interacting with users in relation to acharacteristic of one of the represented business processes.

What is claimed is:
 1. A method for populating an expert systemknowledgebase with an axiom that represents a business process, themethod comprising: a processor of a computer system identifying acorrespondence between a business process and an axiom, where the axiomcomprises a rule identifying how the expert system should respond duringnatural-language interactions with a user, where the axiom comprises asubject, an object, a predicate, and rule relationships among thesubject, the object, and the predicate, where the business processcomprises an input, an output, an action, and process dependencies amongthe input, the output, and the action, where the correspondenceidentifies the subject as corresponding to the input, the object ascorresponding to the output, and the predicate as corresponding to theaction, and where the correspondence further identifies a correspondencebetween each rule relationship and a corresponding process relationship;and the processor representing the business process by adding to adirected graph that represents axioms stored in the knowledgebase: threenodes that respectively represent the subject, the object, and thepredicate, and a set of edges that each connect one pair of the subject,the object, and the predicate, and that each represent one of theprocess dependencies between the connected pair.
 2. The method of claim1, where the axiom is a rule for formulating a natural-language reply toa user question submitted during the natural-language interactions. 3.The method of claim 1, where the axiom is a rule for inferring semanticmeaning from the natural-language interactions as a function of thebusiness process.
 4. The method of claim 1, where the axiom is a rulefor inferring additional rules from the natural-language interactions.5. The method of claim 1, where the axiom is a rule for selecting, inresponse to receiving user input during the natural-languageinteractions, a second axiom stored in the knowledgebase.
 6. The methodof claim 1, where the knowledgebase is automatically populated as afunction of information comprised by the set of business processes. 7.The method of claim 1, where a classification of a set ofclassifications is represented as a first classification node of thedirected graph, where the set of classifications comprises a set ofconcept classifications and a set of activity classifications, where afirst concept classification of the set of concept classificationsassociates an input domain with the input and with the subject, where asecond concept classification of the set of concept classificationsassociates an output domain with the output and with the object, where afirst activity classification of the set of activity classificationsassociates an action domain with the action and with the predicate,where a first classification relationship identifies a dependencyrelationship between a pair of classifications of the set ofclassifications, and where the first classification relationship is afunction of a characteristic of the first business process, and wherethe representing the business process further comprises adding to thedirected graph, as a further function of the characteristic, an edgerepresenting the first classification relationship as a firstclassification dependency of the process dependencies.
 8. The method ofclaim 1, further comprising providing at least one support service forat least one of creating, integrating, hosting, maintaining, anddeploying computer-readable program code in the computer system, wherethe computer-readable program code in combination with the computersystem is configured to implement the identifying and the representing.9. A computer program product, comprising a computer-readable hardwarestorage device having a computer-readable program code stored therein,said program code configured to be executed by a processor of a computersystem to implement a method for populating an expert systemknowledgebase with an axiom that represents a business process, themethod comprising: the processor identifying a correspondence between abusiness process and an axiom, where the axiom comprises a ruleidentifying how the expert system should respond during natural-languageinteractions with a user, where the axiom comprises a subject, anobject, a predicate, and rule relationships among the subject, theobject, and the predicate, where the business process comprises aninput, an output, an action, and process dependencies among the input,the output, and the action, where the correspondence identifies thesubject as corresponding to the input, the object as corresponding tothe output, and the predicate as corresponding to the action, and wherethe correspondence further identifies a correspondence between each rulerelationship and a corresponding process relationship; and the processorrepresenting the business process by adding to a directed graph thatrepresents axioms stored in the knowledgebase: three nodes thatrespectively represent the subject, the object, and the predicate, and aset of edges that each connect one pair of the subject, the object, andthe predicate, and that each represent one of the process dependenciesbetween the connected pair.
 10. The computer program product of claim 9,where the axiom is a rule for formulating a natural-language reply to auser question submitted during the natural-language interactions. 11.The computer program product of claim 9, where the axiom is a rule forinferring semantic meaning from the natural-language interactions as afunction of the business process.
 12. The computer program product ofclaim 9, where the axiom is a rule for inferring additional rules fromthe natural-language interactions.
 13. The computer program product ofclaim 9, where the axiom is a rule for selecting, in response toreceiving user input during the natural-language interactions, a secondaxiom stored in the knowledgebase.
 14. The computer program product ofclaim 9, where a classification of a set of classifications isrepresented as a first classification node of the directed graph, wherethe set of classifications comprises a set of concept classificationsand a set of activity classifications, where a first conceptclassification of the set of concept classifications associates an inputdomain with the input and with the subject, where a second conceptclassification of the set of concept classifications associates anoutput domain with the output and with the object, where a firstactivity classification of the set of activity classificationsassociates an action domain with the action and with the predicate,where a first classification relationship identifies a dependencyrelationship between a pair of classifications of the set ofclassifications, and where the first classification relationship is afunction of a characteristic of the first business process, and wherethe representing the business process further comprises adding to thedirected graph, as a further function of the characteristic, an edgerepresenting the first classification relationship as a firstclassification dependency of the process dependencies.
 15. A computersystem comprising a processor, a memory coupled to said processor, and acomputer-readable hardware storage device coupled to said processor,said storage device containing program code configured to be run by saidprocessor via the memory to implement a method for populating an expertsystem knowledgebase with an axiom that represents a business process,the method comprising: the processor identifying a correspondencebetween a business process and an axiom, where the axiom comprises arule identifying how the expert system should respond duringnatural-language interactions with a user, where the axiom comprises asubject, an object, a predicate, and rule relationships among thesubject, the object, and the predicate, where the business processcomprises an input, an output, an action, and process dependencies amongthe input, the output, and the action, where the correspondenceidentifies the subject as corresponding to the input, the object ascorresponding to the output, and the predicate as corresponding to theaction, and where the correspondence further identifies a correspondencebetween each rule relationship and a corresponding process relationship;and the processor representing the business process by adding to adirected graph that represents axioms stored in the knowledgebase: threenodes that respectively represent the subject, the object, and thepredicate, and a set of edges that each connect one pair of the subject,the object, and the predicate, and that each represent one of theprocess dependencies between the connected pair.
 16. The computer systemof claim 15, where the axiom is a rule for formulating anatural-language reply to a user question submitted during thenatural-language interactions.
 17. The computer system of claim 15,where the axiom is a rule for inferring semantic meaning from thenatural-language interactions as a function of the business process. 18.The computer system of claim 15, where the axiom is a rule for inferringadditional rules from the natural-language interactions.
 19. Thecomputer system of claim 15, where the axiom is a rule for selecting, inresponse to receiving user input during the natural-languageinteractions, a second axiom stored in the knowledgebase.
 20. Thecomputer system of claim 15, where a classification of a set ofclassifications is represented as a first classification node of thedirected graph, where the set of classifications comprises a set ofconcept classifications and a set of activity classifications, where afirst concept classification of the set of concept classificationsassociates an input domain with the input and with the subject, where asecond concept classification of the set of concept classificationsassociates an output domain with the output and with the object, where afirst activity classification of the set of activity classificationsassociates an action domain with the action and with the predicate,where a first classification relationship identifies a dependencyrelationship between a pair of classifications of the set ofclassifications, and where the first classification relationship is afunction of a characteristic of the first business process, and wherethe representing the business process further comprises adding to thedirected graph, as a further function of the characteristic, an edgerepresenting the first classification relationship as a firstclassification dependency of the process dependencies.